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Evaluating geo-tagged Twitter data to analyze tourist flows in Styria, Austria / Johannes Scholz in ISPRS International journal of geo-information, vol 9 n° 11 (November 2020)  

Public [article]  inISPRS International journal of geo-information > vol 9 n° 11 (November 2020) . - n° 681 Titre : Evaluating geo-tagged Twitter data to analyze tourist flows in Styria, Austria Type de document : Article/Communication Auteurs : Johannes Scholz, Auteur ; Janja Jeznik, Auteur Année de publication : 2020 Article en page(s) : n° 681 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] Autriche [Termes IGN] coefficient de corrélation [Termes IGN] contenu généré par les utilisateurs [Termes IGN] distribution spatiale [Termes IGN] méthode fondée sur le noyau [Termes IGN] segmentation sémantique [Termes IGN] tourisme [Termes IGN] Twitter Résumé : (auteur) The research focuses on detecting tourist flows in the Province of Styria in Austria based on crowdsourced data. Twitter data were collected in the time range from 2008 until August 2018. Extracted tweets were submitted to an extensive filtering process within non-relational database MongoDB. Hotspot Analysis and Kernel Density Estimation methods were applied, to investigate spatial distribution of tourism relevant tweets under temporal variations. Furthermore, employing the VADER method an integrated semantic analysis provides sentiments of extracted tweets. Spatial analyses showed that detected Hotspots correspond to typical Styrian touristic areas. Apart from mainly successful sentiment analysis, it pointed out also a problematic aspect of working with multilingual data. For evaluation purposes, the official tourism data from the Province of Styria and federal Statistical Office of Austria played a role of ground truth data. An evaluation with Pearson’s correlation coefficient was employed, which proves a statistically significant correlation between Twitter data and reference data. In particular, the paper shows that crowdsourced data on a regional level can serve as accurate indicator for the behaviour and movement of users. Numéro de notice : A2020-731 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9110681 Date de publication en ligne : 15/11/2020 En ligne : https://doi.org/10.3390/ijgi9110681 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96344 [article]